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Kenneth R. Knapp, Alisa H. Young, Hilawe Semunegus, Anand K. Inamdar, and William Hankins

Abstract

The International Satellite Cloud Climatology Project (ISCCP) began collecting data in the 1980s to help understand the distribution of clouds. Since then, it has provided important information on clouds in time and space and their radiative characteristics. However, it was apparent from some long-term time series of the data that there are some latent artifacts related to the changing satellite coverage over the more than 30 years of the record. Changes in satellite coverage effectively create secular changes in the time series of view zenith angle (VZA) for a given location. There is an inconsistency in the current ISCCP cloud detection algorithm related to VZA: two satellites viewing the same location from different VZAs can produce vastly different estimates of cloud amount. Research is presented that shows that a simple change to the cloud detection algorithm can vastly increase the consistency. This is accomplished by making the cloud–no cloud threshold VZA dependent. The resulting cloud amounts are more consistent between different satellites and the distributions are shown to be more spatially homogenous. Likewise, the more consistent spatial data lead to more consistent temporal statistics.

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Anthony Arguez, Anand Inamdar, Michael A. Palecki, Carl J. Schreck, and Alisa H. Young

Abstract

Climate normals are traditionally calculated every decade as the average values over a period of time, often 30 years. Such an approach assumes a stationary climate, with several alternatives recently introduced to account for monotonic climate change. However, these methods fail to account for interannual climate variability [e.g., El Niño–Southern Oscillation (ENSO)] that systematically alters the background state of the climate similar to climate change. These effects and their uncertainties are well established, but they are not reflected in any readily available climate normals datasets. A new high-resolution set of normals is derived for the contiguous United States that accounts for ENSO and uses the optimal climate normal (OCN)—a 10-yr (15 yr) running average for temperature (precipitation)—to account for climate change. Anomalies are calculated by subtracting the running means and then compositing into 5 ENSO phase and intensity categories: Strong La Niña, Weak La Niña, Neutral, Weak El Niño, and Strong El Niño. Seasonal composites are produced for each of the five phases. The ENSO normals are the sum of these composites with the OCN for a given month. The result is five sets of normals, one for each phase, which users may consult with respect to anticipated ENSO outcomes. While well-established ENSO patterns are found in most cases, a distinct east–west temperature anomaly pattern emerges for Weak El Niño events. This new product can assist stakeholders in planning for a broad array of possible ENSO impacts in a changing climate.

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